Evolutionary conservation and diversification of auditory neural circuits that process courtship songs in Drosophila

Acoustic communication signals diversify even on short evolutionary time scales. To understand how the auditory system underlying acoustic communication could evolve, we conducted a systematic comparison of the early stages of the auditory neural circuit involved in song information processing between closely-related fruit-fly species. Male Drosophila melanogaster and D. simulans produce different sound signals during mating rituals, known as courtship songs. Female flies from these species selectively increase their receptivity when they hear songs with conspecific temporal patterns. Here, we firstly confirmed interspecific differences in temporal pattern preferences; D. simulans preferred pulse songs with longer intervals than D. melanogaster. Primary and secondary song-relay neurons, JO neurons and AMMC-B1 neurons, shared similar morphology and neurotransmitters between species. The temporal pattern preferences of AMMC-B1 neurons were also relatively similar between species, with slight but significant differences in their band-pass properties. Although the shift direction of the response property matched that of the behavior, these differences are not large enough to explain behavioral differences in song preferences. This study enhances our understanding of the conservation and diversification of the architecture of the early-stage neural circuit which processes acoustic communication signals.

Sound stimulus for copulation assay For the sound stimulus, we generated artificial pulse songs with five different IPIs (15, 35, 55, 75, and 95 ms), with the duration of each being 3 s, using the Audacity software ( Fig. 1b and Supplementary Fig. S1). This 3-s sound file is comprised of a repetition of pulses (1-s of pulse burst) and a subsequent 2-s pause as described previously 1 . To generate the pulse bursts with different IPIs, we interspersed one cycle of sine waves with a variable duration of silence. Since a previous study reported no significant effect of IPF, the frequencies of one cycle of the sine wave, on behavioral responses 2 , IPF used to design the artificial pulse songs were 167 Hz for D. melanogaster and 333 Hz for D. simulans to be consistent with the conspecific range of each species (Supplementary Fig. S1) 3 .
The IPFs of the actual playback sounds were ~170 Hz for D. melanogaster and ~310 Hz for D. simulans, both of which were still in the conspecific range of each species 3 .
These IPFs of the playback sounds were estimated from the average time between peak and bottom of the waveform of three recorded pulses. The actual pulses generated by a speaker playback are shown in Supplementary Fig. S1. We played each 3-s sound file repeatedly during the 30-min observation period of the female copulation assay. The mean peak-to-peak amplitude of the resulting particle velocity was 24.8 mm/s.
Larger RMTL values indicate that flies were more likely to copulate.
To compare IPI preferences at the behavioral level between the species, we evaluated the interaction of two covariates, IPI and species, on female receptivity ( Supplementary Fig. S1). Since RMTL analyses are not applicable for two variables, we utilized the Cox proportional hazard model generated in the R 'survival' package (https://cran.r-project.org/web/packages/survival/index.html). In order to maintain the assumption of time-invariant proportional hazards in two assays shown in Fig. 1 and Supplementary Fig. S1, we divided the observation time into two phases, 0-7 min and 7-of the interaction between IPI and species represent a species difference in the effect of IPI on the possibility of copulation events. The hazard value for 35 ms IPI in D.
melanogaster was used as the reference to obtain the HR. HR was calculated for both phases (Supplementary Table S1), with data from only 0-7 min for wild type (Fig. 1d inset) and 7-30 min for calcium imaging strains ( Supplementary Fig. S1b inset) included as graphs. When the HR is greater than 1, an increase of copulation rate in D. simulans at the song carrying a given IPI (15, 55, 75, or 95 -ms) from that at the 35-ms IPI song is higher than that in D. melanogaster.

Confocal microscopy and image processing
Serial optical sections of the antennae and brains were obtained at 0.84-μm (brains) or 0.57-μm (brains and antennae) intervals with an FV1000-D (Fig 2d) or  to perform aligned rank transform analysis of variance (ART ANOVA) tests.
Briefly, image stacks of brains were aligned to a template brain with non-rigid registration using the Computational Morphometry Toolkit 8 (RRID:SCR_002234). The aligned images were reconstructed to 3D images and all neurons except AMMC-B1 were eliminated using VVD Viewer. The extracted neurons were skeletonized with an image processing package Fiji. Subsequently, the skeletonized neurons were vectorized and compared using the nat.nblast R package. One D. melanogaster brain was used as a reference and other D. melanogaster brains and D. simulans brains were analyzed as queries. Scores were statistically evaluated using ART ANOVA.

Generation of D. simulans transgenic strains
To generate a D. simulans nanchung-GAL4 strain, we introduced a plasmid including nanchung regulatory sequences followed by a GAL4 coding sequence. The plasmid was introduced by φC31-mediated recombination into the D.
To label JO neurons in D. simulans, we introduced a nanchung-GAL4 sequence to the TG-S15 attP strain, resulting in only a subsection of JO neurons being labeled ( Supplementary Fig. S2a). Given that different attP landing sites generally drive different levels and patterns of transgene expression 9,10 , the low expression in JO neurons may be due to the genetic environment surrounding the attP landing site. As the nucleotide sequence nanchung promoter region (555kb upstream sequence of nanchung gene) is slightly diversified between D. melanogaster and D. simulans, it is also possible that species differences in the promoter sequence result in differences in driving GAL4expression level.
Calculation of calcium imaging data The frequency response properties of AMMC-B1 neurons were assessed using a generalized linear model (GLM) (Fig. 3f). Frequency and species were used for explanatory variables, and normalized peak response was set as a response variable. A gamma distribution was used for error structure, and a logarithmic function was set as the link function.
To evaluate the IPI response properties and IPF effects of AMMC-B1 neurons ( Fig. 4d, Supplementary Fig. S4), delta (∆) response was calculated as the differences between normalized peak responses at 25 ms IPI and other IPIs. Since all datasets were not rejected by Shapiro-Wilk and Bartlett tests, we used a two-tailed t-test with Bonferroni correction for statistical analysis of ∆responses for the IPI response properties, and a pairwise t-test with Bonferroni correction for the IPF effects, respectively. To analyze the response properties for different frequencies ( Fig. 3e and 3f), we calculated the normalized peak response as where peak response when the fly was exposed to a sound of X-Hz frequency responses. Therefore, we utilized this summation rather than a maximum response across stimuli.
To analyze the response properties for different IPIs (Fig. 4), we calculated the normalized peak response as where peak response with X-ms IPI (peak∆F/FIPI=X) was divided by the summation of peak responses at all IPIs (peak∆F/FTotal). For evaluating the IPF effect of AMMC-B1 neurons ( Supplementary Fig. S4), peak∆F/FTotal of each IPF was calculated separately.
Bayesian hierarchical modeling for fitting  Table 1). Figure  Time window  Explanatory Table S1.

Supplementary Tables
Hazard ratio of copulation assay in females.
(35-X)*(mel-sim) represents the interaction between IPI (35 ms and X ms) and species (D.    Table S5. Starting values of the parameters used for MCMC fitting (See Supplementary Fig. S4).